We rely on dates and times for everything from scheduling meetings to understanding historical events. But can AI truly grasp these fundamental concepts? New research reveals that even the most advanced Large Language Models (LLMs) struggle with temporal reasoning, often misinterpreting dates and making logical errors involving time. Researchers have introduced a new benchmark called DateLogicQA, a cleverly designed set of 190 questions testing how well LLMs handle different date formats, time periods, and types of reasoning. From simple commonsense questions like calculating someone's age at graduation to more complex factual queries about historical events, DateLogicQA exposes AI's weaknesses in dealing with time. The findings are surprising. It turns out how a date is formatted drastically affects how well an LLM understands it. Formats like YYYY, Mon DD are easier for AI to parse than the Julian calendar format (YYYY/DD). More unexpectedly, LLMs seem to be better at reasoning about future dates than past or present ones, potentially highlighting how these models rely on prediction and generation rather than factual recall. The research delves even deeper, examining the internal workings of LLMs. It turns out that AI models represent different time periods with distinct semantic structures, leading to inconsistencies in how they process temporal information. This internal 'representation-level bias,' combined with 'logical-level bias' in their output probabilities, explains why LLMs sometimes make illogical jumps in temporal reasoning. These biases aren't just theoretical; they have real-world implications. Imagine an AI scheduling system that misinterprets a meeting date or a historical research assistant that makes inaccurate claims about timelines. To address these limitations, researchers are exploring improvements to how LLMs are trained, including more diverse temporal data and specialized fine-tuning techniques. They are also investigating methods like Retrieval-Augmented Generation (RAG), which allows LLMs to access external knowledge to supplement their internal understanding of time. While these advancements offer promising solutions, the research underscores the ongoing challenge of creating AI that truly understands the complexities of time, a concept so fundamental to human understanding.